Ultrasound determination of dynamic air bronchogram and associated devices, systems, and methods

ABSTRACT

Ultrasound image devices, systems, and methods are provided. In one embodiment, an ultrasound imaging system includes an interface coupled to an ultrasound imaging component and configured to receive a plurality of image data frames representative of a subject&#39;s body including at least a portion of a lung; a processing component in communication with the interface and configured to determine a metric for each image data frame of the plurality of image data frames based on a threshold comparison; and determine a dynamic air bronchogram (AB) condition of the subject&#39;s body based on a variation across the metrics of the plurality of image data frames. In one embodiment, the processing component is configured to determine differential data frames based on differences across consecutive image data frames of the plurality of image data frames; and determine a dynamic AB condition of the subject&#39;s body based on the differential data frames.

RELATED APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalNo. 62/238,361, filed Feb. 9, 2018, and International Application No.PCT/CN2017/097624, filed Aug. 16, 2017, the entireties of which areincorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to ultrasound imaging and, inparticular, to providing automated systems and methods for identifyingthe presence of a dynamic air bronchogram (AB) indicative of pneumoniabased on lung ultrasound imaging.

BACKGROUND

Pneumonia (PN) is a common disease worldwide, with about 2 million toabout 3 million cases diagnosed annually in the United States. Thesymptoms of PN may include high fever, cough, shortness of breath, chestpain, and/or other respiratory-related symptoms. Physical examinations(e.g., listening to an area over the chest for lung sounds) may not beeffective or reliable for detection of PN at an early stage. One of thecommonly used techniques for PN diagnosis is chest radiography (CXR).However, bedside CXR may provide limited image quality. In addition, CXRis a time-consuming procedure for emergency situations and theinterpretation of bedside CXR may be challenging, requiring extensiveradiologic experience to avoid misinterpretation of the wide spectrum ofpleural and pulmonary diseases. Further, final results may have a highvariability among radiologists. An improved approach to diagnosing PNcompared to bedside CXR is to use thoracic computerized tomography (CT)imaging. However, CT imaging can be expensive and may have a higherradiation exposure than CXR. Thus, CT imaging may not be suitable forroutine-diagnostics in emergency department, critical care units (CCUs),or intensive care units (ICUs), especially for young children andpregnant women.

Ultrasound imaging, especially point-of-care ultrasound (POC-US) atbedside, has gain popularity ICUs and emergency situations for varioustypes of diagnostics. Recent studies have shown that lung ultrasoundimaging can be useful and effective in diagnosis of PN with a relativelyhigh accuracy. For example, ultrasound images may not show useful oradequate information for a normal aerated lung, whereas a lungconsolidation may appear as bright spots or bright structures underultrasound imaging. In addition, the appearance of brightness (B)-linesin ultrasound images may be indicative of PN at an early stage. Thus,POC lung ultrasound imaging can be useful and attractive for PNdiagnosis. However, ultrasound imaging-based PN diagnosis may require awell-trained or experienced physician or clinician to analyze andinterpret acquired lung image. Currently, there is no effective tool inguiding inexperience users for screening and diagnosis of PN.

SUMMARY

While existing ultrasound lung imaging has proved useful for diagnosisof PN, there remains a clinical need for improved systems and techniquesfor providing low-cost and easily interpreted PN diagnostic results.Embodiments of the present disclosure provide mechanisms for diagnosingPN by identifying and indicating the presence of a dynamic airbronchogram (AB) based on lung ultrasound images in an automated manner.Under lung ultrasound imaging, dynamic ABs correspond to bright spots orpixels that change or move over time due to respiratory cycles. In oneembodiment, dynamic AB is identified based on a variation of a number ofbright spots or pixels across a number of image frames over time. Inanother embodiment, dynamic AB is identified based on a temporalintensity variation of bright spots or pixels across a number of imageframes over time. In yet another embodiment, the appearance of bronchialtrees corresponding to dynamic ABs are enhanced in lung ultrasoundimages by accumulating differences across image data frames over time.

In one embodiment, an ultrasound imaging system is provided. Theultrasound imaging system includes an interface coupled to an ultrasoundimaging component and configured to receive a plurality of image dataframes representative of a subject's body including at least a portionof a lung; a processing component in communication with the interfaceand configured to determine a metric for each image data frame of theplurality of image data frames based on a threshold comparison; anddetermine a dynamic air bronchogram (AB) condition of the subject's bodybased on a variation across the metrics of the plurality of image dataframes.

In some embodiments, each image data frame includes a plurality of pixelvalues representing pixel intensities of an image of the subject's body.In some embodiments, the plurality of image data frames represent imagesof the subject's body across a time period including at least onerespiratory cycle. In some embodiments, the processing component isconfigured to determine the metric for each image data frame of theplurality of image data frames by determining a number of the pluralityof pixel values in each image data frame that satisfies a threshold. Insome embodiments, the processing component is configured to determinethe dynamic AB condition by determining a ratio between a maximum of themetrics and a minimum of the metrics; and determining the dynamic ABcondition based on the ratio. In some embodiments, the processingcomponent is further configured to identify a region-of-interest (ROI)from the plurality of image data frames corresponding to the at least aportion of the lung; and determine the metrics based on the ROI. In someembodiments, the ultrasound imaging system further includes a displaycomponent configured to display a result of the dynamic AB condition. Insome embodiments, the ultrasound imaging system further includes anultrasound imaging probe including the ultrasound imaging component; theprocessing component; and a display component configured to display aresult of the dynamic AB condition.

In one embodiment, an ultrasound imaging system is provided. Theultrasound imaging system includes an interface coupled to an ultrasoundimaging component and configured to receive a plurality of image dataframes representative of a subject's body including at least a portionof a lung; a processing component in communication with the interfaceand configured to determine differential data frames based ondifferences across consecutive image data frames of the plurality ofimage data frames; determine an accumulated data frame based on a sum ofthe differential data frames; and determine a dynamic air bronchogram(AB) condition of the subject's body based on the accumulated dataframe.

In some embodiments, the plurality of image data frames represent imagesof the subject's body across a time period including at least onerespiratory cycle. In some embodiments each image data frame includes aplurality of pixel values representing pixel intensities of an image ofthe subject's body. In some embodiments, each differential data frameincludes a plurality of difference values, wherein the processingcomponent is further configured to determine each differential dataframe by determining each difference value of the plurality ofdifference values by determining an absolute difference between a pixelvalue of a first data frame of the plurality of image data frames and apixel value of a second data frame of the plurality of image dataframes, wherein the first data frame is adjacent to the second dataframe, and wherein the pixel value of the first data frame and the pixelvalue of the second data frame represents a same sub-portion of the atleast a portion of the lung. In some embodiments, each differential dataframe includes a first plurality of pixel values, wherein theaccumulated data frame includes a plurality of sum values, and whereinthe processing component is further configured to determine the dynamicAB condition by determining each sum value of the plurality of sumvalues by accumulating a second plurality of pixel values across thedifferential data frames, wherein the second plurality of pixel valuesacross the differential data frames represent a same portion of thesubject's body; and determining the dynamic AB condition based on theplurality of sum values. In some embodiments, the ultrasound imagingsystem of claim 9, further comprising a display component configured todisplay the accumulated data frame.

In one embodiment, an ultrasound imaging system is provided. Theultrasound imaging system includes an interface coupled to an ultrasoundimaging component and configured to receive a plurality of image dataframes representative of a subject's body including at least a portionof a lung; a processing component in communication with the interfaceand configured to identify a subset of data from the plurality of imagedata frames based on a threshold comparison; and determine a dynamic airbronchogram (AB) condition of the subject's body based on a temporalvariation across the subset of data.

In some embodiments, each image data frame of the plurality of imagedata frames includes a plurality of pixel values representing pixelintensities of an image of the subject's body. In some embodiments, theplurality of image data frames represent images of the subject's bodyacross a time period including at least one respiratory cycle. In someembodiments, the processing component is configured to identify thesubset of data by selecting one or more pixel values from each imagedata frame of the plurality of image data frames corresponding to a samesub-portion of the at least a portion of the lung and satisfying athreshold; determine a first value for each image data frame of theplurality of image data frames based on the one or more pixel values ofa corresponding image data frame; and determine the dynamic AB conditionbased on a ratio between a maximum of the first values and a minimum ofthe first values. In some embodiments, the processing component isconfigured to apply a filter across the first values prior to thedetermining the dynamic AB condition. In some embodiments, theultrasound imaging system further includes a display componentconfigured to display a result of the dynamic AB condition.

In one embodiment, a method of ultrasound imaging diagnostic isprovided. The method includes receiving, from an ultrasound imagingprobe, a plurality of image data frames associated with a subject's bodyincluding at least a portion of a lung; determining a first value foreach image data frame of the plurality of image data frames based on athreshold comparison; and determine a dynamic air bronchogram (AB)condition of the subject's body based on a variation across the firstvalues of the plurality of image data frames.

In some embodiments, each image data frame includes a plurality of pixelvalues representing pixel intensities of an image of the subject's body.In some embodiments, the plurality of image data frames representsimages of the subject's body across a time period including at least onerespiratory cycle. In some embodiments, the determining the first valueincludes determining a number of the plurality of pixel values in eachimage data frame that satisfies a threshold, and wherein the determiningthe dynamic AB condition includes determining a ratio between a maximumof the first values and a minimum of the first values; and determiningthe dynamic AB condition based on the ratio. In some embodiments, themethod further includes identifying a region of interest (ROI) from theplurality of image data frames corresponding to the at least a portionof the lung; and determining the first values based on the ROI. In someembodiments, the method further includes displaying, at a displaycomponent, a result of the dynamic AB condition.

Additional aspects, features, and advantages of the present disclosurewill become apparent from the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present disclosure will be describedwith reference to the accompanying drawings, of which:

FIG. 1 is a schematic diagram of an ultrasound imaging system, accordingto aspects of the present disclosure.

FIG. 2 illustrates the dynamics of ABs from an expiratory period to aninspiratory period, according to aspects of the present disclosure.

FIG. 3 illustrates the dynamics of ABs in an area of a lungconsolidation over time, according to aspects of the present disclosure.

FIG. 4 is a schematic diagram illustrating a dynamic AB detectionscheme, according to aspects of the present disclosure.

FIG. 5 is an image frame illustrating dynamic ABs, according to aspectsof the present disclosure.

FIG. 6 is a graph illustrating variations of a number of bright pixelsacross a number of image frames over time in the presences of dynamicABs, according to aspects of the present disclosure.

FIG. 7 is an image frame illustrating static ABs, according to aspectsof the present disclosure.

FIG. 8 is a graph illustrating variations of a number of bright pixelsacross a number of image frames over time in the absence of dynamic ABs,according to aspects of the present disclosure.

FIG. 9 is a schematic diagram illustrating a dynamic AB detectionscheme, according to aspects of the present disclosure.

FIG. 10 is a schematic diagram illustrating a bronchial tree enhancementscheme, according to aspects of the present disclosure.

FIG. 11A is an image frame illustrating a bronchial tree at a timeinstant, according to aspects of the present disclosure.

FIG. 11B is an image frame illustrating a bronchial tree at another timeinstant, according to aspects of the present disclosure.

FIG. 11C is a differential image frame illustrating a difference betweena pair of consecutive image frames including a bronchial tree, accordingto aspects of the present disclosure.

FIG. 11D is an accumulated image frame illustrating an enhancedbronchial tree, according to aspects of the present disclosure.

FIG. 12 is a graph illustrating variations of a number of bright spotsacross a number of image frames over time in the presence of dynamicABs, according to aspects of the present disclosure.

FIG. 13 is a flow diagram of a dynamic AB detection method, according toaspects of the present disclosure.

FIG. 14 is a flow diagram of a dynamic AB detection method, according toaspects of the present disclosure.

FIG. 15 is a flow diagram of a bronchial tree enhancement method,according to aspects of the present disclosure.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It is nevertheless understood that no limitation tothe scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, and methods, and anyfurther application of the principles of the present disclosure arefully contemplated and included within the present disclosure as wouldnormally occur to one skilled in the art to which the disclosurerelates. In particular, it is fully contemplated that the features,components, and/or steps described with respect to one embodiment may becombined with the features, components, and/or steps described withrespect to other embodiments of the present disclosure. For the sake ofbrevity, however, the numerous iterations of these combinations will notbe described separately.

FIG. 1 is a schematic diagram of an ultrasound imaging system 100,according to aspects of the present disclosure. The system 100 is usedfor scanning an area or volume of a patient's body. The system 100includes an ultrasound imaging probe 110 in communication with a host130 over a communication interface or link 120. The probe 110 includes atransducer array 112, a beamformer 114, a processing component 116, adisplay 117, and a communication interface 118. The host 130 includes adisplay 132, a communication interface 136, and a communicationinterface 136.

The transducer array 112 emits ultrasound signals towards an anatomicalobject 105 and receives echo signals reflected from the object 105 backto the transducer array 112. The transducer array 112 may includeacoustic elements arranged in a one-dimensional (1D) array or in atwo-dimensional (2D) array. The beamformer 114 is coupled to thetransducer array 112. The beamformer 114 controls the transducer array112, for example, for transmission of the ultrasound signals andreception of the ultrasound echo signals. The beamformer 114 providesimage signals to the processing component 116 based on the response orthe received ultrasound echo signals. The beamformer 114 may includemultiple stages of beamforming. The beamforming can reduce the number ofsignal lines for coupling to the processing component 116. In someembodiments, the transducer array 112 in combination with the beamformer114 may be referred to as an ultrasound imaging component.

The processing component 116 is coupled to the beamformer 114. Theprocessing component 116 generates image data from the image signals.The processing component 116 may be implemented as a combination ofsoftware components and hardware components. In an embodiment, theprocessing component 116 may be implemented on a field programmable gatearray (FPGA) and may include programmable state machines to control theprocessing and conversion of the image signals to the image data. Forexample, the processing component 116 may perform filtering and/orquadrature demodulation to condition the image signals. The processingcomponent 116 may perform analytic detection on the filtered signals.The display 117 is coupled to the processing component 116. The display132 may be a screen or any suitable display integral with the housing ofthe probe 110. The display 117 may be configured to display the resultsof the analytic detection.

The communication interface 118 is coupled to the processing component116. The communication interface 118 transmits the image signals to thehost 130 via the communication link 120. At the host 130, thecommunication interface 136 may receive the image signals. The host 130may be any suitable computing and display device, such as a workstation,a personal computer (PC), a laptop, a tablet, or a mobile phone. Thecommunication link 120 may be any suitable communication link. Forexample, the communication link 120 may be a wired link, such as auniversal serial bus (USB) link or an Ethernet link. Alternatively, thecommunication link 120 nay be a wireless link, such as an ultra-wideband(UWB) link, an Institute of Electrical and Electronics Engineers (IEEE)802.11 WiFi link, or a Bluetooth link.

The processing component 134 is coupled to the communication interface136. The processing component 134 may be implemented as a combination ofsoftware components and hardware components. The processing component134 may include a central processing unit (CPU), a digital signalprocessor (DSP), an application-specific integrated circuit (ASIC), acontroller, a FPGA device, another hardware device, a firmware device,or any combination thereof configured to perform the operationsdescribed herein. The processing component 134 may also be implementedas a combination of computing devices, e.g., a combination of a DSP anda microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration. The processing component 134 can be configured to performimage processing and image analysis for various diagnostic modalities.The display 132 is coupled to the processing component 134. The display132 may be a monitor or any suitable display. The display 132 isconfigured to display images and/or diagnostic results processed by theprocessing component 134.

The system 100 can be configured for dynamic AB detection. For example,the object 105 may correspond to a portion of a patient's body includingat least a portion of the patient's lung. In one embodiment, the probe110 transmits the image signals (e.g., the echo signals) received fromthe transducer array 112 to the host 130. The processing component 134can detect dynamic AB s from ultrasound images and indicate a positivedynamic AB detection and a location of the detected dynamic AB or anegative dynamic AB detection on the display 132. The processingcomponent 134 can identify dynamic ABs based on a variation of a numberof bright spots or pixels across a number of image frames over time.Alternatively, the processing component 134 can identify dynamic ABsbased on a temporal intensity variation of bright spots or pixels acrossa number of image frames over time. The processing component 134 canenhance the appearance or visibility of bronchial trees in ultrasoundimages. Mechanisms for detecting dynamic ABs and enhancing bronchialtrees are described in greater detail herein. In another embodiment, theprocessing component 116 on the probe 110 can be configured to performdynamic AB detection eliminating the need of a host. In such anembodiment, dynamic AB detection results can be displayed on theintegral display 117.

One of the ultrasound imaging sign for identifying PN is the detectionof a positive dynamic AB within a lung consolidation. An AB is a tubularoutline of an airway visible under lung ultrasound imaging due to thefilling of surrounding alveoli by fluid or inflammatory exudates. Somestudies have shown that the combination of a lung consolidation withdynamic AB accounts for about 70 percent (%) of total PN cases inclinical practices.

FIG. 2 illustrates the dynamics of ABs from an expiratory period to aninspiratory period, according to aspects of the present disclosure. InFIG. 2, the image 210 is acquired during an expiratory period of apatient and the image 220 is acquired during an inspiratory period ofthe patient. As shown in the image 210, ABs 212 appear as tubular brightstructures. Similarly in the image 220, the ABs 214 appear as tubularbright structures. As can be observed, the appearance of ABs (e.g.,bright structures) varies over time, for example, from the ABs 212 inthe expiratory period to the ABs 214 in the inspiratory period. Inaddition, the images 210 and 220 show movements of punctiform ABs 216(e.g., bright spots) to punctiform ABs 218.

FIG. 3 illustrates the dynamics of ABs in an area of a lungconsolidation over time, according to aspects of the present disclosure.In FIG. 3, the x-axis represents time in some constant units and they-axis represents depths in some constant units. The image 300illustrates a real-time ultrasound scan line as a function of time. Forexample, the transducer array 112 is used to generate one beam directedalong a scan line towards an area within a lung consolidation and repeatat some time intervals. The image 300 shows the echo signals receivedfrom transducer array 112 corresponding to the emitted beam. In theimage 300, the bright cyclic patterned lines show the movements or shiftof ABs, such as the ABs 212, 214, 216, and 218, in a periodic manner.The cyclic or periodic pattern corresponds to the expiratory period 310and inspiratory period 320 as marked in the image 300.

As can be seen in FIGS. 2 and 3, dynamic ABs can be detected based onintensity variations of pixels across image frames over one or morerespiratory cycles. However, real-time visual identification of dynamicABs may be difficult or unreliable due to the fast movements or motionsof the dynamic ABs while the lung in the background of the ultrasoundimages may still be moving during respiration, where a respiratory cyclemay span a duration of about 3 seconds to about 6 seconds. In addition,the clinician that performs the lung ultrasound imaging (e.g., at aPOC-US) may not necessarily be trained for interpreting lung ultrasoundimages for dynamic AB detection.

Some studies conduct quantitative analysis for dynamic AB identificationbased on a measure of mean pixel intensities. While such quantitativeanalysis shows promising results (e.g., with a detection sensitivity ofabout 93%), the quantitative analysis requires expertise to identify andisolate regions corresponding to a patient's lung. In addition, thequantitative analysis approach may only perform well for a large area oflung consolidation and may miss detection of a small lung consolidation(e.g., extending less than about 1 centimeter (cm)) or PN at an earlystage.

FIG. 4 is a schematic diagram illustrating an automatic dynamic ABdetection scheme 400, according to aspects of the present disclosure.The scheme 400 can be employed by the system 100 for dynamic ABdetection. Specifically, the scheme 400 can be implemented by theprocessing component 134 on the host 130 or the processing component 116on the probe 110. In some embodiments, the implementation of the scheme400 can be divided between the host 130 and the probe 110.

The scheme 400 begins with receiving a number of image frames 410. Theimage frames 410 may be generated by using the probe 110, whereultrasound signal energy is emitted from the transducer array 112towards a patient's body (e.g., the object 105) and echo signals arereceived by the transducer array 112 forming the image frames 410. Theprobe 110 can be positioned on the patient's body to obtain an anteriorchest view (e.g., from above the chest area) or a lateral chest view(e.g., from the side of the chest area) including at least some portionsof the patient's lungs. Each image frame 410 may include a plurality ofpixel values (e.g., amplitudes) representing pixel intensities of animage of the patient's body. The image frames 410 may be taken over atime period including at least one respiratory cycle (e.g., anexpiratory period and an inspiratory period). The image frames 410 areshown as Frame(i) to Frame (i+N) representing images of the patient'sbody from a time instant (i) to a time instant (i+N), where i and N arepositive integers.

The image frames 410 can include other portions of the patient's body inaddition to the patient's lung. The scheme 400 can apply aregion-of-interest (ROI) identification component 420 to the imageframes 410 to identify portions of the image frames corresponding to thepatient's lung for subsequent processing. One approach to identifying anarea of a lung is based on a pleural line identification. A pleural linemay appear as a bright line in an ultrasound image (shown in FIG. 5) andthe region below the pleural line may correspond to the lung. Afteridentifying the portions corresponding to the patient's lung, the ROIidentification component 420 can identify an ROI in an area of the lungwith a potential lung consolidation for the subsequent processing. Forexample, the ROI identification component 420 may perform bulkbackground motion detection using block-matching on a frame-by-framebasis, for example, based correlations across the image frames 410.

After identifying the ROI, the ROI identification component 420 may usethe initial frame (e.g., Frame (i)) as an anchoring frame to align orregister subsequent image frames 410 (e.g., Frame (i+1) to Frame (i+N))to the initial image frame 410 based on the background motioninformation for subsequent operations described below. The alignment orregistration allows the operations to be performed on the same portionof the lung for each image frame 410.

After identifying the ROI, a threshold component 430 can be applied toeach image frame 410. The threshold component 430 determines the numberof pixels in the ROI of a corresponding image frame 410 that are above apredetermined threshold. The number of pixels above the threshold may berepresented by a count value 432. The pixels that are above thethreshold may correspond to bright spots as shown in the ABs 212, 214,216, and 218. The predetermined threshold may be configured to anysuitable value depending on the dynamic range of the pixel values andthe amplitude and/or pixel distribution in the ROI. In an embodiment,the dynamic range of the pixel intensity values may be configured to bebetween about 0 and about 255. In such an embodiment, the threshold maybe configured to a value between about 40 to about 80.

After determining the number of bright pixels in each image frame 410, amaximum component 440 and a minimum component 450 can be applied to thecount values 432. The maximum component 440 determines a maximum value442 of the count values across the image frames 410. The minimumcomponent 450 determines a minimum value 452 of the count values 432across the image frames 410. In an embodiment, the maximum value 442 andthe minimum value 452 can be normalized such that the maximum value 442has a value of one.

After determining the maximum value 442 and the minimum value 452, an ABindex component 460 can be applied to determine a dynamic AB diagnosticresult. As described above, dynamic ABs are shown as bright structures(e.g., the ABs 212 and 214) or bright spots (e.g., the ABs 216 and 218)varying over time. The AB index component 460 identifies the dynamics ofthe ABs by computing a ratio between the maximum value 442 and theminimum value 452. The ratio may be referred to as an AB index. Forexample, the AB index component 460 may compare the ratio to apredetermined threshold. When there is a large variation between themaximum value 442 and the minimum value 452, a dynamic AB condition maybe positive. Conversely, when there is a small variation between themaximum value 442 and the minimum value 452, a dynamic AB condition maybe negative and a static AB condition may be present. The presence of adynamic AB condition may indicate a high likelihood of PN, while thepresence of a static AB condition may indicate a high likelihood ofatelectasis (e.g., lung collapse without infection) or other lungdiseases. The dynamic AB diagnostic result can be displayed on thedisplay 132 and/or the display 117. In addition, the result may indicatethe location of the dynamic AB condition in the lung.

FIG. 5 is an ultrasound image 500 illustrating dynamic ABs, according toaspects of the present disclosure. The image 500 may be acquired usingthe system 100. The image 500 may represent an image of a patient's body(e.g., the object 105) including an area of a lung. The image 500illustrates the presence of ABs 512 and lung consolidation 506 in anarea of the patient's lung. As marked in the image 500, a pleuralinterface 502 or pleural boundary appears as a bright line across theimage 500. The area below the pleural interface 502 corresponds to thepatient's lung. The area above the pleural interface 510 corresponds tothe patient's chest wall 504.

FIG. 6 is a graph 600 illustrating variations of a number of brightpixels across a number of image frames over time in the presences ofdynamic ABs, according to aspects of the present disclosure. The x-axisrepresents frame number. The y-axis represents normalized number ofpixels in an ROI within image frames that are above a predeterminedintensity threshold. The graph 600 is generated using the scheme 400.The plot 610 shows the variations of the number of pixels above thethreshold (e.g., the count values 432 in a normalized form) across anumber of image frames (e.g., the image frames 410) over time in theregion of the dynamic ABs 512 shown in the image 500. As can be seen,the plot 610 varies between about 0.84 to about 1. The large differencebetween the minimum (e.g., 0.84) and the maximum (e.g., 1) and thecyclic patterns observed in the plot 610 may indicate the presence of adynamic AB condition. The cyclic or periodic patterns may correspond torespiratory cycles of the patient.

FIG. 7 is an ultrasound image 700 illustrating static ABs, according toaspects of the present disclosure. The image 700 may be acquired usingthe system 100. The image 700 may represent an image of a patient's body(e.g., the object 105) including an area of a lung. The image 700illustrates the presence of static ABs 712 in an area of the patient'slung.

FIG. 8 is a graph 800 illustrating variations of a number of brightpixels across a number of image frames over time in the absence ofdynamic ABs, according to aspects of the present disclosure. The x-axisrepresents frame number. The y-axis represents normalized number ofpixels in an ROI within image frames that are above a predeterminedintensity threshold. The graph 800 is generated using the scheme 400.The plot 810 shows the variations of the number of pixels above thethreshold (e.g., the count values 432 in a normalized form) across anumber of image frames (e.g., the image frames 410) over time in theregion of the static ABs 712 of the image 700. As can be seen, the plot810 is relatively static varying between about 0.987 to about 1. Thesmall difference between the minimum (e.g., 0.987) and the maximum(e.g., 1) and the relatively static pattern observed in the plot 810 mayindicate the presence of a static AB condition (e.g., an atelectasiscondition).

FIG. 9 is a schematic diagram illustrating a dynamic AB detection scheme900, according to aspects of the present disclosure. The scheme 900 canbe employed by the system 100 for dynamic AB detection. Specifically,the scheme 900 can be implemented by the processing component 134 on thehost 130 or the processing component 116 on the probe 110. In someembodiments, the implementation of the scheme 900 can be divided betweenthe host 130 and the probe 110.

The scheme 900 begins with receiving a number of image frames 910similar to the image frames 410. For example, each image frame 910includes pixel intensity values representing an image of a patient'sbody including at least a portion of the patient's lung. The scheme 900can apply an ROI identification component 920 to the image frames 910.The ROI identification component 920 may be substantially similar to theROI identification component 420. The ROI identification component 920may identify a subset of data or pixels from the image frames 910 fordynamic AB condition determination. The identification may includeselecting one or more pixel values from each image frame 910corresponding to the same portion of the patient's lung around a lungconsolidation. The ROI identification component 920 may output imagedata subsets 930 including pixels within the ROI. The subsets 930 areshown as Frame (i, k) to Frame (i+N, k) representing a subset k withinFrame (i) to a subset k within Frame (i+1), respectively. When eachsubset 930 includes one pixel value, the pixel value is represented byan intensity value 932. When each subset 930 includes more than onepixel values, a spatial filter may be applied to each subset 930 toproduce an average intensity value 932.

After identifying the subsets 930, a maximum component 940 and a minimumcomponent 950 can be applied to the intensity values 932. The maximumcomponent 940 determines a maximum value 942 of the intensity values932. The minimum component 950 determines a minimum value 952 of theintensity values 932. In an embodiment, the maximum value 942 and theminimum value 952 can be normalized such that the maximum value 942 hasa value of 1. In some embodiments, a temporal filter (e.g., a smoothingfilter) can be applied to the subsets 930, for example, to obtain anaverage value over a number of frames, before determining the maximumvalue 942 and the minimum value 952.

After determining the maximum value 942 and the minimum value 952, atemporal intensity variation determination component 960 can be appliedto determine a dynamic AB diagnostic result. The temporal intensityvariation determination component 960 determines a temporal intensityvariation across the subsets 930 over time. The temporal intensityvariation determination component 960 can determine a ratio between themaximum value 942 and the minimum value 952. The temporal intensityvariation determination component 960 can compare the ratio to apredetermined threshold and determine whether a dynamic AB condition ispresent based on the threshold comparison. Similar to the scheme 400, alarge variation between the maximum value 942 and the minimum value 952is indicative of a positive dynamic AB condition and a small variationbetween the maximum value 942 and the minimum value 952 is indicative ofa negative dynamic AB condition.

FIG. 10 is a schematic diagram illustrating a bronchial tree enhancementscheme 1000, according to aspects of the present disclosure. The scheme1000 can be employed by the system 100 to enhance bronchial trees withina lung consolidation in lung ultrasound images for dynamic AB diagnosis.Specifically, the scheme 1000 can be implemented by the processingcomponent 134 on the host 130.

The scheme 1000 begins with receiving a number of image frames 1010similar to the image frames 410 and 910. For example, each image frame1010 includes pixel intensity values representing an image of apatient's body including at least a portion of the patient's lung. Thescheme 1000 applies a difference component 1020 to each pair of adjacentor consecutive image frames 1010 (e.g., Frame (i) and Frame (i+1)). Thedifference component 1020 computes a difference between the adjacentimage frames 1010 to produce a differential image frame 1022. Forexample, the difference component 1020 subtracts the pixel values in theFrame (i) by the pixel values in the Frame (i+1) on a pixel-by-pixelbasis. The pixel values of the Frame (i) and the pixel values of theFrame (i+1) correspond to the same sub-portion of the patient's lung.

An absolute component 1030 can be applied to the pixel values in thedifferential image frames 1022 to produce differential image frames 1032with absolute difference pixel values. Subsequently, a sum component1040 can be applied to accumulate the differential image frames 1032 toproduce an accumulated image frame 1042. For example, the sum component1040 sums the pixel values of the differential image frames 1032 on apixel-by-pixel basis. When the image frames 1010 include a bronchialtree, the appearance or visibility of the bronchial tree may be enhancedin the accumulated image frame 1042 (shown in FIG. 11D below). Thedetection of the bronchial tree indicates a positive dynamic ABcondition.

FIGS. 11A-11D illustrates various image frames corresponding to variousstages in the scheme 1000 described above with respect to FIG. 10. Forexample, the scheme 1000 is applied to eighty sequential ultrasoundimage frames, represented by Frame(1), Frame(2), . . . , Frame (80).FIG. 11A is an image frame 1110 illustrating a bronchial tree 1112 at atime instant, according to aspects of the present disclosure. The imageframe 1110 may be similar to the image frames 410, 910, and 1010. Theimage frame 1110 may represent a first image frame (e.g., Frame (1)) ofthe eighty sequential image frames. As shown, the bright Y-shapedbranching structure in the middle portion of the image frame 1110corresponds to the bronchial tree 1112 within a lung consolidation of apatient. The bright curved horizontal structure at the bottom of theimage frame 1110 corresponds to the patient's spine.

FIG. 11B is an image frame 1120 illustrating a bronchial tree 1112 atanother time instant, according to aspects of the present disclosure.For example, the image frame 1120 may represent the last image frame(e.g., Frame (80)) of the eight sequential image frames.

FIG. 11C is a differential image frame 1130 illustrating a differencebetween a pair of consecutive image frames including a bronchial tree1112, according to aspects of the present disclosure. For example, thedifferential image frame 1130 may represent a differential image frame1032 at the output of the absolute component 1030 in the scheme 1000.The differential image frame 1130 may be computed by subtracting Frame(80) from Frame (79) pixel by pixel.

FIG. 11D is an accumulated image frame 1140 illustrating an enhancedbronchial tree 1112, according to aspects of the present disclosure. Forexample, the image frame 1140 may represent an accumulated image frame1042 at the output of the sum component 1040 in the scheme 1000.Comparing the accumulated image frame 1140 to the originally acquiredimage frames 1110 and 1120, the appearance or visibility of thebronchial tree 1112 is enhanced in the accumulated image frame 1140.

FIG. 12 is a graph 1200 illustrating variations of a number of brightpixels across a number of image frames over time in the presence ofdynamic ABs, according to aspects of the present disclosure. The x-axisrepresents frame number. The y-axis represents normalized number ofpixels in an ROI within image frames that are above a predeterminedintensity threshold. The graph 1200 is generated by applying the scheme400 to the eight sequential image frames used for illustrating thescheme 1000 in FIG. 11. The plot 1210 shows variations of the normalizednumber of pixels above a predetermined threshold across a number ofimage frames (e.g., the image frames 1010) over time in the region ofthe bronchial tree 1112 shown in the image frames 1110 and 1120. As canbe seen, the plot 1210 varies between about 0.53 to about 1. The largedifference between the minimum (e.g., 0.53) and the maximum (e.g., 1)and the periodic waveform observed in the plot 1210 may indicate thepresence of a dynamic AB condition.

FIG. 13 is a flow diagram of a dynamic AB detection method 1300,according to aspects of the present disclosure. Steps of the method 1300can be executed by a computing device (e.g., a processor, processingcircuit, and/or other suitable component) of an ultrasound imagingprobe, such as the probe 110, or a host such as the host 130. The method1300 may employ similar mechanisms as in the scheme 400 as describedwith respect to FIG. 4. As illustrated, the method 1300 includes anumber of enumerated steps, but embodiments of the method 1300 mayinclude additional steps before, after, and in between the enumeratedsteps. In some embodiments, one or more of the enumerated steps may beomitted or performed in a different order.

At step 1310, the method 1300 includes receiving a plurality of imagedata frames (e.g., the image frames 410, 910, 1010, 1110, and 1120)representative of a subject's body (e.g., the object 105) including atleast a portion of a lung. The subject's body may be a human body or ananimal body.

At step 1320, the method 1300 includes determining a metric (e.g., thecount values 432) for each image data frame of the plurality of imagedata frames, for example, using the threshold component 430.

At step 1330, the method 1300 includes determining a dynamic ABcondition of the subject's body based on a variation across the metricsof the plurality of image data frames. For example, the maximumcomponent 440 can be applied to the metrics to compute a maximum value(e.g., the maximum value 442) of the metrics and the minimum component450 can be applied to the metrics to compute a minimum value (e.g., theminimum value 452) of the metrics. Subsequently, the AB index component460 can be applied to compute a ratio between the maximum value and theminimum value and compare the ratio to a predetermine threshold. Whenthe ratio satisfy the predetermine threshold, a positive dynamic ABcondition may be present. When the ratio fails to satisfy thepredetermined threshold, a dynamic AB condition may be absent.

FIG. 14 is a flow diagram of a dynamic AB detection method 1400,according to aspects of the present disclosure. Steps of the method 1400can be executed by a computing device (e.g., a processor, processingcircuit, and/or other suitable component) of an ultrasound imagingprobe, such as the probe 110, or a host such as the host 130. The method1400 may employ similar mechanisms as in the scheme 900 as describedwith respect to FIG. 9. As illustrated, the method 1400 includes anumber of enumerated steps, but embodiments of the method 1400 mayinclude additional steps before, after, and in between the enumeratedsteps. In some embodiments, one or more of the enumerated steps may beomitted or performed in a different order.

At step 1410, the method 1400 includes receiving a plurality of imagedata frames (e.g., the image frames 410, 910, 1010, 1110, and 1120)representative of a subject's body (e.g., the object 105) including atleast a portion of a lung. The subject's body may be a human body or ananimal body.

At step 1420, the method 1400 includes identifying a subset of data(e.g., the subsets 930) from the plurality of image data frames based ona threshold comparison, for example, using the ROI identificationcomponent 920.

At step 1430, the method 1400 includes determining a dynamic ABcondition of the subject's body based on a temporal variation across thesubset of data. For example, the subset of data may include a portion(e.g., one or more pixel values) of each image data frame and a spatialfilter can be applied to a corresponding portion of each image frame todetermine a first value (e.g., the intensity values 932) for each imagedata frame. The maximum component 940 can be applied to the first valuesto compute a maximum value (e.g., the maximum value 942) of the firstvalues and the minimum component 950 can be applied to the first valuesto compute a minimum value (e.g., the minimum value 952) of the firstvalues. Subsequently, the temporal intensity variation determinationcomponent 960 can be applied to compute a ratio between the maximumvalue and the minimum value and compare the ratio to a predeterminethreshold. When the ratio satisfy the predetermine threshold, a positivedynamic AB condition may be present. When the ratio fails to satisfy thepredetermined threshold, a dynamic AB condition may be absent.

FIG. 15 is a flow diagram of a bronchial tree enhancement method 1500,according to aspects of the present disclosure. Steps of the method 1500can be executed by a computing device (e.g., a processor, processingcircuit, and/or other suitable component) of a host such as the host130. The method 1500 may employ similar mechanisms as in the scheme 1000as described with respect to FIG. 10. As illustrated, the method 1500includes a number of enumerated steps, but embodiments of the method1500 may include additional steps before, after, and in between theenumerated steps. In some embodiments, one or more of the enumeratedsteps may be omitted or performed in a different order.

At step 1510, the method 1500 includes receiving a plurality of imagedata frames (e.g., the image frames 410, 910, 1010, 1110, and 1120)representative of a subject's body (e.g., the object 105) including atleast a portion of a lung. The subject's body may be a human body or ananimal body.

At step 1520, the method 1500 includes determining differential dataframes (e.g., the differential image frames 1032) based on differencesacross consecutive image data frames of the plurality of image dataframes.

At step 1530, the method 1500 includes determining an accumulated dataframe (e.g., the accumulated image frame 1042) based on a sum of thedifferential data frames.

At step 1540, the method 1500 includes determining a dynamic ABcondition of the subject's body based on the accumulated data frame. Forexample, the accumulated data frame shows an enhanced appearance orvisibility of the bronchial tree in a location within a lungconsolidation. Thus, the determination may be on the observation of thebronchial tree.

Aspects of the present disclosure can provide several benefits. Forexample, the automatic detection of dynamic ABs without the need for awell-trained clinician to interpret ultrasound lung images allowpoint-of-care ultrasound (POC-US) imaging to be used for PN screeningand diagnosis. The automatic dynamic AB detection can produce adiagnostic result in a short duration of time. Thus, PN examination timecan be shortened when compared to a full chest PN examination. Inaddition, the disclosed embodiments provide a standardized testingprotocol. The use of the standardized test protocol can produce moreconsistent diagnostic results than the subjective evaluations andanalysis under different physicians and clinicians. The standardizedtest protocol can be carried out easily for screening and suitable foruse in time critical situations (e.g., during an emergency). Further,the enhanced display of bronchial tree can assist a physician toidentify a PN location quickly and easily. The disclosed embodiments aresuitable for use with pediatric patients and/or pregnant women whereradiation exposure is a concern.

Persons skilled in the art will recognize that the apparatus, systems,and methods described above can be modified in various ways.Accordingly, persons of ordinary skill in the art will appreciate thatthe embodiments encompassed by the present disclosure are not limited tothe particular exemplary embodiments described above. In that regard,although illustrative embodiments have been shown and described, a widerange of modification, change, and substitution is contemplated in theforegoing disclosure. It is understood that such variations may be madeto the foregoing without departing from the scope of the presentdisclosure. Accordingly, it is appropriate that the appended claims beconstrued broadly and in a manner consistent with the presentdisclosure.

1. An ultrasound imaging system, comprising: an interface coupled to anultrasound imaging component and configured to receive a plurality ofimage data frames representative of a subject's body including at leasta portion of a lung, each image data frame of the plurality of imagedata frames including a plurality of pixel values representing pixelintensities of an image of the subject's body; a processing component incommunication with the interface and configured to: determine a countmetric for each image data frame of the plurality of image data framesby counting a number of the plurality of pixel values in each image dataframe that satisfies a threshold; and determine a dynamic airbronchogram (AB) condition of the subject's body based on a variationacross the count metrics of the plurality of image data frames. 2.(canceled)
 3. The ultrasound imaging system of claim 1, wherein theplurality of image data frames represent images of the subject's bodyacross a time period including at least one respiratory cycle. 4.(canceled)
 5. The ultrasound imaging system of claim 1, wherein theprocessing component is configured to determine the dynamic AB conditionby: determining a ratio between a maximum of the count metrics and aminimum of the count metrics; and determining the dynamic AB conditionbased on the ratio.
 6. The ultrasound imaging system of claim 1, whereinthe processing component is further configured to: identify aregion-of-interest (ROI) from the plurality of image data framescorresponding to the at least a portion of the lung; and determine thecount metrics based on the ROI.
 7. The ultrasound imaging system ofclaim 1, further comprising a display component configured to display aresult of the dynamic AB condition.
 8. The ultrasound imaging system ofclaim 1, further comprising an ultrasound imaging probe including: theultrasound imaging component; the processing component; and a displaycomponent configured to display a result of the dynamic AB condition. 9.An ultrasound imaging system, comprising: an interface coupled to anultrasound imaging component and configured to receive a plurality ofimage data frames representative of a subject's body including at leasta portion of a lung, each image data frame of the plurality of imagedata frames includes a first plurality of pixel values representingpixel intensities of an image of the subject's body; a processingcomponent in communication with the interface and configured to:determine differential data frames based on differences acrossconsecutive image data frames of the plurality of image data frames bysubtracting a pixel value of a first image data frame of the pluralityof image data frames from a pixel value of a second image data frame ofthe plurality of image data frames, the second image data frame beingadjacent to the first image data frame; determine an accumulated dataframe based on a sum of the differential data frames; and determine adynamic air bronchogram (AB) condition of the subject's body based onthe accumulated data frame.
 10. The ultrasound imaging system of claim9, wherein the plurality of image data frames represent images of thesubject's body across a time period including at least one respiratorycycle.
 11. (canceled)
 12. The ultrasound imaging system of claim 9,wherein each differential data frame includes a plurality of differencevalues, wherein the processing component is further configured todetermine a first differential data frame of the differential dataframes by: determining a difference value for the first differentialdata frame by determining an absolute value of a result of thesubtraction, wherein the pixel value of the first image data frame andthe pixel value of the second image data frame represents a samesub-portion of the at least a portion of the lung.
 13. The ultrasoundimaging system of claim 9, wherein each differential data frame includesa second plurality of pixel values, wherein the accumulated data frameincludes a plurality of sum values, and wherein the processing componentis further configured to determine the dynamic AB condition by:determining each sum value of the plurality of sum values byaccumulating a second plurality of pixel values across the differentialdata frames, wherein the third plurality of pixel values across thedifferential data frames represent a same portion of the subject's body;and determining the dynamic AB condition based on the plurality of sumvalues.
 14. The ultrasound imaging system of claim 9, further comprisinga display component configured to display the accumulated data frame.15. An ultrasound imaging system, comprising: an interface coupled to anultrasound imaging component and configured to receive a plurality ofimage data frames representative of a subject's body including at leasta portion of a lung across a time period, each image data frame of theplurality of image data frames including a plurality of pixel valuesrepresenting pixel intensities of an image of the subject's body; aprocessing component in communication with the interface and configuredto: identify a subset of data from the plurality of image data frames byselecting one or more pixel values from each image data frame of theplurality of image data frames corresponding to a same sub-portion ofthe at least a portion of the lung; determine an intensity metric foreach image data frame from the subset of data and determine a dynamicair bronchogram (AB) condition of the subject's body based on a temporalvariation across the subset of data by comparing a maximum of theintensity metrics across the time period to a minimum of the intensitymetrics across the time period.
 16. (canceled)
 17. The ultrasoundimaging system of claim 15, wherein the time period includes at leastone respiratory cycle.
 18. The ultrasound imaging system of claim 15,wherein the processing component is configured to: determine the dynamicAB condition based on a ratio between the maximum of the intensitymetrics and the minimum of the of the intensity metrics.
 19. Theultrasound imaging system of claim 18, wherein the processing componentis configured to apply a filter across the intensity metrics prior tothe determining the dynamic AB condition.
 20. The ultrasound imagingsystem of claim 15, further comprising a display component configured todisplay a result of the dynamic AB condition. 21.-26. (canceled)